Multivariate kernel density estimation: Multivariate kernel density estimation
Description
Multivariate kernel density estimation.
Usage
mkde(x, h = NULL, thumb = "silverman")
Arguments
x
A matrix with Euclidean (continuous) data.
h
The bandwidh value. It can be a single value, which is turned into a vector and
then into a diagonal matrix, or a vector which is turned into a diagonal matrix.
If you put this NULL then you need to specify the "thumb" argument below.
thumb
Do you want to use a rule of thumb for the bandwidth parameter? If no, set h
equal to NULL and put "estim" for maximum likelihood cross-validation, "scott"
or "silverman" for Scott's and Silverman's rules of thumb respectively.
Value
A vector with the density estimates calculated for every vector.
Details
The multivariate kernel density estimate is calculated with a (not necssarily
given) bandwidth value.
References
Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives.
The kedd R package.
M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.
B.W. Silverman (1986). Density estimation for statistics and data analysis, pages 76-78.